Open AccessPrediction of chaotic time series based on fuzzy model
Author(s) -
Hongwei Wang,
MA Guang-fu
Publication year - 2004
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.53.3293
Subject(s) - chaotic , computer science , fuzzy logic , series (stratigraphy) , nonlinear system , time series , neuro fuzzy , kalman filter , adaptive neuro fuzzy inference system , line (geometry) , control theory (sociology) , algorithm , fuzzy control system , artificial intelligence , mathematics , machine learning , physics , paleontology , geometry , control (management) , quantum mechanics , biology
For dynamic systems with complex, illconditioned, or nonlinear characteristics, the fuzzy model based on fuzzy sets is very useful to describe the properties of the dynamic systems using fuzzy inference rules. Modeling and prediction of nonlinear systems using fuzzy modeling is discussed in this paper. First, the fuzzy space of input variables is partitioned by means of online fuzzy competitive learning. Further, the parameters of fuzzy model are estimated by means of Kalman filtering algorithm. To illustrate the performance of the proposed method, simulations on the chaotic MackeyGlass time series prediction are performed. Combining either offline or online learning with the proposed method, we can show that the chaotic MackeyGlass time series are accurately predicted, and demonstrate the effectiveness.